Archives for April 2010

A team led by Hari Prasad, Alex Halderman, and Rop Gonggrijp released today a technical paper detailing serious security problems with the electronic voting machines (EVMs) used in India.

The independent Electoral Commission of India, which is generally well respected, has dealt poorly with previous questions about EVM security. The chair of the Electoral Commission has called the machines “infallible” and “perfect” and has rejected any suggestion that security improvements are even possible. I hope the new study will cause the EC to take a more realistic approach to EVM security.

The researchers got their hands on a real Indian EVM which they were able to examine and analyze. They were unable to extract the software running in the machine (because that would have required rendering the machine unusable for elections, which they had agreed not to do) so their analysis focused on the hardware. They were able to identify several attacks that manipulated the hardware, either by replacing components or by clamping something on to a chip on the motherboard to modify votes. They implemented demonstration attacks, actually building proof-of-concept substitute hardware and vote-manipulation devices.

Perhaps the most interesting aspect of India’s EVMs is how simple they are. Simplicity is a virtue in security as in engineering generally, and researchers (including me) who have studied US voting machines have advocated simplifying their design. India’s EVMs show that while simplicity is good, it’s not enough. Unless there is some way to audit or verify the votes, even a simple system is subject to manipulation.

The ball is now in the Election Commission’s court. Let’s hope that they take steps to address the EVM problems, to give the citizens of the world’s largest democracy the transparent and accurate elections they deserve.

Orin discusses two laws that specifically shield journalists from being the target of a search, the California Reporter’s Shield Law, found jointly at California Penal Code 1524(g) and California Evidence Code 1070, and the federal Privacy Protection Act (PPA), 42 U.S.C. 2000aa. Both laws were written to limit the impact of Zurcher v. Stanford Daily, a U.S. Supreme Court case authorizing the use of a warrant to search a newspaper’s offices. The Supreme Court decided Zurcher in 1978, and Congress enacted the PPA in 1980 (and amended it in unrelated ways in 1996). I’m not sure when the California law was enacted, but I bet it’s of similar vintage. In other words, all of the rules that govern police searches of news offices were created in the age of typewriters, desks, filing cabinets, and stacks of paper.

Now, flash forward thirty years. The police who searched Jason Chen’s home seized the following: A macbook, HP server, two Dell desktop computers, iPad, ThinkPad, two MacBook Pros, IOmega NAS, three external hard drives, and three flash drives. They also seized other storage-containing devices, including two digital cameras and two smart phones. If Jason Chen’s computing habits are anything like mine, the police likely seized many terabytes of disk space, storing hundreds of thousands (millions?) of files, containing information stretching back years. And they took all of this information to investigate an alleged crime (the sale of the iPhone prototype) that could not have happened more than 37 days before the search (the iPhone was found on March 18th), which they learned about from a blog post published four days before the search.

I’m deeply concerned about overbreadth as the police begin to search through these terabytes of information. The police now possess, intermingled with the evidence of the alleged crime they are investigating, hundreds of thousands of documents belonging to a journalist/blogger that are utterly irrelevant to their investigation. Jason Chen has been blogging for Gizmodo since 2006, and he’s probably written hundreds of stories. The police likely have thousands of email messages revealing confidential sources, detailing meetings, and trading comments with editors, and thousands of other documents bearing notes from interviews, drafts of articles, and other sensitive information. Because of Chen’s beat, some of these documents probably reveal secrets of great economic and business value in the Silicon Valley. Under traditional, outmoded Fourth Amendment rules, the police can read every single document they possess, so long as they intend only to look for evidence of the crime, and under the “plain view rule,” they can use any evidence they find of other, unrelated crimes in court against Chen or anyone else.

If the California state courts share my concerns about overbreadth, they should consider embracing the very sensible rules for search warrants for computer hard drives (in any case, not just those involving journalists) adopted last year by the Ninth Circuit in United States v. Comprehensive Drug Testing. To paraphrase, in cases involving the search and seizure of computers, the Ninth Circuit requires five things: (1) the government must waive the plain view rule, meaning they must agree not to use evidence of crimes other than the one under investigation that led to the warrant; (2) the government must wall off the forensic experts who search the hard drive from the investigating the case; (3) the government must explain the “actual risks of destruction of information” they would face if they weren’t allowed to seize entire computers; (4) the government must use a search protocol to designate what information they can give to the investigating agents; and (5) the government must destroy or return non-responsive data.

These rules are especially needed when the target of a police search is a journalist (in fact, they may not go far enough). And these rules may be required under Zurcher. In justifying the search of the newspaper’s offices in Zurcher, the Supreme Court agreed that when the Fourth Amendment’s search and seizure rules collide with First Amendment values, like freedom of the press, the “Fourth Amendment must be applied with ‘scrupulous exactitude.'” The court went on to explain why ordinary search warrants for news offices (remember, back in the age of paper files) meet this heightened standard:

There is no reason to believe, for example, that magistrates cannot guard against searches of the type, scope, and intrusiveness that would actually interfere with the timely publication of a newspaper. Nor, if the requirements of specificity and reasonableness are properly applied, policed, and observed, will there be any occasion or opportunity for officers to rummage at large in newspaper files or to intrude into or to deter normal editorial and publication decisions.

When the California state courts combine this thirty-year-old statement of the law with the modern realities of terabyte storage devices, they should hold that the Fourth Amendment requires magistrate judges to play an integral and active role in the administration of the search of Jason Chen’s computers and other storage devices. At the very least, the courts should forbid the police from looking at any file timestamped before March 18, 2010, and in addition, they should force the police to comply with the Comprehensive Drug Testing rules. In the terabyte age, these rules are necessary at a minimum to prevent the police from interfering with a free press.

Last week I wrote about needle-in-a-haystack problems, in which it’s hard to find the solution but if somebody tells you the solution it’s easy to verify. A commenter asked whether such problems are related to the P vs. NP problem, which is the most important unsolved problem in theoretical computer science. It turns out that they are related, and that needle-in-a-haystack problems are a nice framework for explaining the P vs. NP problem, which few non-experts seem to understand.

Stated simply, the P vs. NP problem asks whether needle-in-a-haystack computational problems can exist. To understand what this means, we need to take a short detour into computer science theoryland. It’s perfectly safe, but stay close to the group so you don’t wander off into a finite field…. Okay, let’s meet P and NP.

P is the set of problems that can be solved efficiently. The precise theoretical definition of “efficient” is a bit subtle: we define the “size” of a problem to be the number of characters needed to write down the problem; and we say a solution method is efficient if, when the problem is large, the method can finish in a length of time that is less than the problem size raised to some power.

For example, suppose we are given two N-digit numbers, A and B, and a 2N-digit number C, and we’re asked whether A times B equals C. It requires 4N characters to write down the problem, one character for each digit in each of the numbers. We can solve the problem by multiplying A and B, using the multiplication method we learned in school, and then comparing the result to C. The multiplication will take us roughly N-squared steps, and the comparison is faster than that. So the solution time will grow roughly as N-squared, and if N is large this is less than the third power of the problem size (i.e., less than 4N raised to the third power). Therefore multiplication is in P, which matches our intuition that we know how to multiply efficiently.

Here’s another example, the “traveling salesperson problem” (TSP): given a list of cities, a table showing the airfare between each pair of cities, and a total travel budget, is there an itinerary that visits every city on the list and returns to where it started, without exceeding the total travel budget? One way to solve this problem is to try out all of the possible itineraries, and see if there’s one that is cheap enough. That works, but it is not efficient, because its running time grows faster than any fixed power of the problem size. No efficient algorithm for the TSP is known. There might be one, but if there is one we haven’t discovered it yet. So we don’t know if the TSP is in P.

You might have guessed by now that P stands for “polynomial”, as in “solvable in polynomial time”. That is indeed the origin of the name P. And you might go on to guess that NP stands for “not polynomial” — but that would be wrong. If you must know, NP stands for “nondeterministic polynomial”. I won’t bother explaining what “nondeterministic” means here, because that has confused generations of students. Instead, let’s use an alternative, but equivalent, definition of NP.

NP is the set of problems having yes/no answers, for which, whenever the correct answer is “yes”, there is some “hint” that allows us to verify the “yes” answer efficiently. Think about the TSP: if somebody tells you a cheap itinerary, you can verify efficiently that that itinerary visits all of the cities, ends where it started, and costs less than the travel budget. So the TSP is in NP.

Multiplication is in NP as well. Given any hint (“booga booga”, say) we can verify that A times B equals C, by simply ignoring the hint, and then multiplying and comparing as above. By a similar argument, any problem in P must also be in NP.

You can see now how this connects with needles and haystacks. If a problem is in NP but not in P, it’s like a needle-in-a-haystack problem: we can verify the answer efficiently if we’re given a hint, but without a hint we can’t find the answer efficiently. So asking whether needle-in-a-haystack problems exist is exactly the same as asking whether P is different from NP.

Are P and NP different? We don’t know. Consider the Traveling Salesperson Problem. We know it’s in NP, because we can verify the answer efficiently given a hint, but we don’t know if it’s in P. We don’t know if the TSP can be solved efficiently — we haven’t found an efficient solution yet but we can’t rule out the possibility that somebody will discover one tomorrow. A lot of people have tried really hard for a long time to find an efficient solution, so most experts tend to assume that no efficient solution is possible — but the experts have been wrong before.

If, on the other hand, P and NP turn out to be equal, the consequences would be huge. For example, much of cryptography would collapse. Decrypting a message is supposed to be a needle-in-a-haystack problem—easy if you have a hint (i.e., the secret decryption key) but hard otherwise.

It’s annoying, to say the least, that such a basic question about information remains unanswered. For decades theorists have been trying to scale the P vs. NP mountain from various directions,without success. The rest of us have gotten accustomed to assuming that needle-in-a-haystack problems exist, and hoping for the best.

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